Nothing
## ----setup, include=FALSE, cache=FALSE--------------------------------------------------
library(knitr)
# set global chunk options
# opts_chunk$set(fig.path='figure/minimal-', fig.align='center', fig.show='hold')
options(formatR.arrow=TRUE,width=90)
knitr::opts_chunk$set(dpi=100, eval = FALSE)
## ----warning = FALSE, message = FALSE---------------------------------------------------
# library("dplyr")
# library("FRK")
# library("ggplot2")
# library("IDE")
# library("sp")
# library("spacetime")
## ----echo = FALSE, message=FALSE, warning=FALSE-----------------------------------------
# library("ggplot2")
# library("gridExtra")
## ----message=FALSE----------------------------------------------------------------------
# SIM1 <- simIDE(T = 10, nobs = 100, k_spat_invariant = 1)
## ----results = 'hide', fig.keep = 'none'------------------------------------------------
# print(SIM1$g_truth)
# print(SIM1$g_obs)
## ----message = FALSE--------------------------------------------------------------------
# IDEmodel <- IDE(f = z ~ s1 + s2,
# data = SIM1$z_STIDF,
# dt = as.difftime(1, units = "days"),
# grid_size = 41)
## ----eval = FALSE-----------------------------------------------------------------------
# fit_results_sim1 <- fit.IDE(IDEmodel,
# parallelType = 1)
## ----results = 'hide', fig.keep = 'none'------------------------------------------------
# show_kernel(fit_results_sim1$IDEmodel)
## ---------------------------------------------------------------------------------------
# fit_results_sim1$IDEmodel$get("k") %>% unlist()
## ---------------------------------------------------------------------------------------
# fit_results_sim1$IDEmodel$get("betahat")
## ---------------------------------------------------------------------------------------
# abs_ev <- eigen(fit_results_sim1$IDEmodel$get("M"))$values %>% abs()
# summary(abs_ev)
## ---------------------------------------------------------------------------------------
# ST_grid_df <- predict(fit_results_sim1$IDEmodel)
## ----results = 'hide', fig.keep = 'none'------------------------------------------------
# gpred <- ggplot(ST_grid_df) + # Plot the predictions
# geom_tile(aes(s1, s2, fill=Ypred)) +
# facet_wrap(~t) +
# scale_fill_distiller(palette="Spectral", limits = c(-0.1,1.4)) +
# coord_fixed(xlim=c(0, 1), ylim = c(0, 1))
#
# gpredse <- ggplot(ST_grid_df) + # Plot the prediction s.es
# geom_tile(aes(s1, s2, fill=Ypredse)) +
# facet_wrap(~t) +
# scale_fill_distiller(palette="Spectral") +
# coord_fixed(xlim=c(0, 1), ylim = c(0, 1))
## ----echo = FALSE, fig.keep = 'none', results = 'hide', message = FALSE-----------------
# library("gridExtra")
# g <- grid.arrange(SIM1$g_truth + scale_x_continuous(breaks = c(0,0.5)) +
# scale_fill_distiller(palette = "Spectral", limits = c(0.1,1.4)),
# SIM1$g_obs + scale_x_continuous(breaks = c(0,0.5)) +
# scale_fill_distiller(palette = "Spectral", limits = c(0.1,1.4)),
# gpred + scale_x_continuous(breaks = c(0,0.5)) +
# scale_fill_distiller(palette = "Spectral", limits = c(0.1,1.4)),
# gpredse + scale_x_continuous(breaks = c(0,0.5)) ,
# nrow = 2)
# ggsave(g, file = "./img/Chapter_5/IDEsimresults.png", width = 12, height = 10, dpi = 300)
## ----echo= FALSE, eval = FALSE----------------------------------------------------------
# s1_pred <- s2_pred <- seq(0,1,length.out = 71)
# st_grid <- expand.grid(s1 = s1_pred,
# s2 = s2_pred,
# date = unique(time(SIM1$z_STIDF)))
# pred_grid <- STIDF(sp = SpatialPoints(st_grid[,c("s1","s2")]),
# time = st_grid$date,
# data = st_grid %>% select(-s1, -s2, -date))
#
# ## Predict using prior guesses
# ST_grid_df <- predict(fit_results_sim1$IDEmodel,
# newdata = pred_grid) %>%
# as.data.frame()
## ---------------------------------------------------------------------------------------
# SIM2 <- simIDE(T = 15, nobs = 1000, k_spat_invariant = 0)
## ----results = 'hide', fig.keep = 'none'------------------------------------------------
# print(SIM2$g_truth)
# print(SIM2$g_obs)
## ----eval = FALSE-----------------------------------------------------------------------
# show_kernel(SIM2$IDEmodel, scale = 0.2)
## ---------------------------------------------------------------------------------------
# mbasis_1 <- auto_basis(manifold = plane(), # functions on the plane
# data = SIM2$z_STIDF, # data
# nres = 1, # 1 resolution
# type = 'bisquare') # type of functions
## ---------------------------------------------------------------------------------------
# kernel_basis <- list(thetam1 = constant_basis(),
# thetam2 = constant_basis(),
# thetam3 = mbasis_1,
# thetam4 = mbasis_1)
## ---------------------------------------------------------------------------------------
# IDEmodel <- IDE(f = z ~ s1 + s2 + 1,
# data = SIM2$z_STIDF,
# dt = as.difftime(1, units = "days"),
# grid_size = 41,
# kernel_basis = kernel_basis)
## ----eval = FALSE-----------------------------------------------------------------------
# fit_results_sim2 <- fit.IDE(IDEmodel,
# parallelType = 1,
# itermax = 400)
## ----eval = FALSE-----------------------------------------------------------------------
# show_kernel(fit_results_sim2$IDEmodel)
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